PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid
Abstract A major challenge in effective cancer treatment is the variability of drug responses among patients. Patient-derived organoids greatly preserve the genetic and histological characteristics even the drug sensitivities of primary tumor tissues, therefore provide a compelling approach to predi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-01082-6 |
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| _version_ | 1849387817630695424 |
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| author | Yuru Zhou Quanhui Dai Yanming Xu Shuang Wu Minzhang Cheng Bing Zhao |
| author_facet | Yuru Zhou Quanhui Dai Yanming Xu Shuang Wu Minzhang Cheng Bing Zhao |
| author_sort | Yuru Zhou |
| collection | DOAJ |
| description | Abstract A major challenge in effective cancer treatment is the variability of drug responses among patients. Patient-derived organoids greatly preserve the genetic and histological characteristics even the drug sensitivities of primary tumor tissues, therefore provide a compelling approach to predict clinical outcome. However, the individual organoid culture and following drug response test are time and cost-consuming, which hinders the potential clinical application. Here, we developed PharmaFormer, a clinical drug response prediction model based on custom Transformer architecture and transfer learning. PharmaFormer was initially pre-trained with the abundant gene expression and drug sensitivity data of 2D cell lines, and was then finalized through a model further fine-tuned with the limited organoid pharmacogenomic data accumulated at the present stage. Our results demonstrate that PharmaFormer, integrating both pan-cancer cell lines and organoids of a specific type of tumor, provides a dramatically improved accurate prediction of clinical drug response. This study highlights that advanced AI models combined with biomimetic organoid models will accelerate precision medicine and future drug development. |
| format | Article |
| id | doaj-art-012c99fac7f44b0a9045cf4ac1b835df |
| institution | Kabale University |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-012c99fac7f44b0a9045cf4ac1b835df2025-08-20T03:42:29ZengNature Portfolionpj Precision Oncology2397-768X2025-08-019111110.1038/s41698-025-01082-6PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoidYuru Zhou0Quanhui Dai1Yanming Xu2Shuang Wu3Minzhang Cheng4Bing Zhao5School of Basic Medical Sciences, Institute of Biomedical Innovation, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityZ Lab, bioGenous BIOTECHInstitute of Organoid Technology, Kunming Medical UniversityZ Lab, bioGenous BIOTECHSchool of Basic Medical Sciences, Institute of Biomedical Innovation, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversitySchool of Basic Medical Sciences, Institute of Biomedical Innovation, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityAbstract A major challenge in effective cancer treatment is the variability of drug responses among patients. Patient-derived organoids greatly preserve the genetic and histological characteristics even the drug sensitivities of primary tumor tissues, therefore provide a compelling approach to predict clinical outcome. However, the individual organoid culture and following drug response test are time and cost-consuming, which hinders the potential clinical application. Here, we developed PharmaFormer, a clinical drug response prediction model based on custom Transformer architecture and transfer learning. PharmaFormer was initially pre-trained with the abundant gene expression and drug sensitivity data of 2D cell lines, and was then finalized through a model further fine-tuned with the limited organoid pharmacogenomic data accumulated at the present stage. Our results demonstrate that PharmaFormer, integrating both pan-cancer cell lines and organoids of a specific type of tumor, provides a dramatically improved accurate prediction of clinical drug response. This study highlights that advanced AI models combined with biomimetic organoid models will accelerate precision medicine and future drug development.https://doi.org/10.1038/s41698-025-01082-6 |
| spellingShingle | Yuru Zhou Quanhui Dai Yanming Xu Shuang Wu Minzhang Cheng Bing Zhao PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid npj Precision Oncology |
| title | PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid |
| title_full | PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid |
| title_fullStr | PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid |
| title_full_unstemmed | PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid |
| title_short | PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid |
| title_sort | pharmaformer predicts clinical drug responses through transfer learning guided by patient derived organoid |
| url | https://doi.org/10.1038/s41698-025-01082-6 |
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